Off-policy evaluation (OPE) in reinforcement learning allows one to evaluate novel decision
policies without needing to conduct exploration, which is often costly or otherwise infeasible …

In many experimental and observational studies, the outcome of interest is often difficult or
expensive to observe, reducing effective sample sizes for estimating average treatment …

We consider the problem of constructing bounds on the average treatment effect (ATE) when
unmeasured confounders exist but have bounded influence. Specifically, we assume that …

Abstract The Highly-Adaptive least absolute shrinkage and selection operator (LASSO)
Targeted Minimum Loss Estimator (HAL-TMLE) is an efficient plug-in estimator of a pathwise …

The hal9001 R package provides a computationally efficient implementation of the highly
adaptive lasso (HAL), a flexible nonparametric regression and machine learning algorithm …

We consider estimation of a functional parameter of a realistically modeled data distribution
based on observing independent and identically distributed observations. The highly …

We study a multivariate version of trend filtering, called Kronecker trend filtering or KTF, for
the case in which the design points form a lattice in $ d $ dimensions. KTF is a natural …

This work considers targeted maximum likelihood estimation (TMLE) of treatment effects on
absolute risk and survival probabilities in classical time‐to‐event settings characterized by …

Inverse‐probability‐weighted estimators are the oldest and potentially most commonly used
class of procedures for the estimation of causal effects. By adjusting for selection biases via …

Debiased machine learning estimators for nonparametric inference of smooth functionals of
the data-generating distribution can suffer from excessive variability and instability. For this …